Human posture recognition for estimation of human body condition

Wei Quan, Jinseok Woo, Yuichiro Toda, Naoyuki Kubota

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Human posture recognition has been a popular research topic since the development of the referent fields of human-robot interaction, and simulation operation. Most of these methods are based on supervised learning, and a large amount of training information is required to conduct an ideal assessment. In this study, we propose a solution to this by applying a number of unsupervised learning algorithms based on the forward kinematics model of the human skeleton. Next, we optimize the proposed method by integrating particle swarm optimization (PSO) for optimization. The advantage of the proposed method is no pre-training data is that required for human posture generation and recognition. We validate the method by conducting a series of experiments with human subjects.

Original languageEnglish
Pages (from-to)519-527
Number of pages9
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume23
Issue number3
DOIs
Publication statusPublished - May 1 2019

Keywords

  • Growing neural gas
  • Human posture recognition
  • Human-robot interaction
  • Particle swarm optimization

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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